Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Abstract

dTechnological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n ‫؍‬ 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.F resh produce presents a unique food safety challenge due to the absence of a kill step between harvest and consumption. An increase in recalls and reported outbreaks linked to fresh produce over the past decade (1-3) have been associated with consumer avoidance of products linked to outbreaks (4, 5). This trend can negatively affect growers and the produce industry (4-6). For example, following a 2011 listeriosis outbreak in the United States associated with fresh cantaloupe (7), cantaloupe consumption dropped 53% nationwide (6). The prevention of produce contamination in production environments is therefore a concern for growers, the produce industry, and public health professionals. To develop effective prevention strategies, it is important to understand the ecological processes and environmental factors that affect foodborne pathogen prevalence in produce production environments. Technological advancements, such as geographic information systems (GIS), have the potential to drastically improve our ability to examine these processes and to develop novel tools for ensuring the safety of fresh produce.Numerous studies (8-21) have examined the ecology of foodborne pathogens in agricultural environments, and several (22-27) have used GIS and geospatial analysis. For example, Chapin...

Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production

“…For instance, in their survey of the microbial quality of the South Nation River Basin, Wilkes et al (2009) found a negative association between the likelihood of L. monocytogenes isolation and E. coli levels. While expected, our failure to identify an association between L. monocytogenes isolation and fecal indicators is interesting given the identification of positive associations between L. monocytogenes isolation and sources of human (e.g., campgrounds, wastewater discharge sites) and livestock (e.g., dairy farms) fecal contamination in this and other studies (Watkins and Sleath, 1981;Dijkstra, 1982;Paillard et al, 2005;Lyautey et al, 2007;Odjadjare et al, 2010;Strawn et al, 2013a;Weller et al, 2016). The failure to identify an association between L. monocytogenes and fecal indicators but the identification of an association between L. monocytogenes and sources of fecal contamination may be due to the fact L. monocytogenes is a microbe adapted to living in non-host environments (Vivant et al, 2013) but the FIBs used here are not (Lee et al, 2008;Bae and Wuertz, 2009).…”

“…This study is novel due to the diversity of data types used (e.g., weather data, land use data from federal databases, data scraped from Google and government permits, field-collected water quality data), and the computational approaches used to generate these data. For instance, this study calculated flow path distances that account for topography and represent the physical distance a contaminant travels from its source to the sampling site; the Euclidean distance measures used in previous studies (e.g., Strawn et al, 2013a;Weller et al, 2016) do not capture this complexity. However, it is also important to recognize the limitations associated with spatial data.…”

“…Thus, to improve growers' ability to identify and address on-farm food safety hazards, targeted approaches that account for this variation are needed. Due to the availability of spatial data (e.g., from government databases, Google), analyses that utilize such data can facilitate identification of factors associated with foodborne pathogen detection in agricultural water (Benjamin et al, 2013;Strawn et al, 2013a;Weller et al, 2015aWeller et al, , 2016; these factors can then be used to develop the aforementioned targeted approaches. Past studies have shown that microbial water quality is affected by the ecological context unique to each water source (e.g., upstream landscape) as well as conditions (e.g., weather, physicochemical water quality) at time of water use (e.g., McEgan et al, 2013a;Bradshaw et al, 2016;Weller et al, 2020).…”

Adjacent terrestrial landscapes impact the biogeographical pattern of soilEscherichia coliin produce fields by modifying the importance of environmental selection and dispersal

“…In addition to E. coli, such proximity effects of forest have also been reported for Listeria monocytogenes and other Listeria species. Weller et al (60) found that with a 100m increase in the distance of a sampling site from forests, the likelihood of L. monocytogenes and other Listeria species isolation in croplands decreased by 14% and 16%, respectively.…”

Assessment of Biological Soil Amendments of Animal Origin Use, Research Needs, and Extension Opportunities in Organic Production

“…Eighteen outbreaks were caused by organic foods from 1992 to 2014 in the U.S., 54% of outbreaks occurred from 2010 to 2014, 44% were associated with produce (e.g., alfalfa sprouts, grape tomatoes), and Salmonella spp and E. coli O157:H7 were the most common pathogens (Harvey et al, 2016).The contamination of produce commodities can occur pre-harvest through the application of raw and/or untreated manure, contaminated agricultural water, direct or indirect (e.g., soil-splash events) contact with contaminated soil or deposition of fecal material from domesticated animals and/or wildlife (Ingham et al, 2004;Olaimat and Holley, 2012;Sharma and Reynnells, 2016). Moreover, fresh produce that is consumed raw or with a minimal processing step presents a unique food safety risk due to the absence of a kill step between harvest and consumption (Olaimat and Holley, 2012;Weller et al, 2016). Therefore, it is crucial that raw manure application, compost processing, and application practices be adequate to minimize the risk of potential crop contamination (Cieslak et al, 1993;Natvig et al, 2002).…”